2020
DOI: 10.3389/fmars.2020.00558
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Regional Movements of Reef Manta Rays (Mobula alfredi) in Seychelles Waters

Abstract: The decline in numbers of reef manta rays (Mobula alfredi) throughout their range has highlighted the need for improved information on their spatial ecology in order to design effective conservation strategies for vulnerable populations. To understand their patterns of movement in Seychelles, we used three techniques-archival pop-up satellite tags, acoustic tags, and photo-identification-and focussed on the aggregation at D'Arros Island and St. Joseph Atoll within the Amirantes Group. M. alfredi were photograp… Show more

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Cited by 26 publications
(30 citation statements)
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“…For example, basking sharks tracked in the English Channel displayed patterns of normal DVM in deep, well-stratified waters and reverse DVM in shallow, inner-shelf areas near thermal fronts, with such changes in behaviour between habitats likely mimicking those of zooplankton (Sims et al, 2005). Similarly, locational differences in DVM patterns have been recorded by reef manta rays, with normal DVM recorded around the islands and atolls of the Chagos archipelago (Andrzejaczek et al, 2020), and reverse DVM in both the coastal and offshore areas of the Red Sea (Braun et al, 2014), and the islands and atolls of Seychelles (Peel et al, 2020). Such diversity in foraging modes enables flexibility as these animals encounter spatial and/or temporal changes in the zooplanktonic prey community.…”
Section: Dvm In Filter-feeding Elasmobranchsmentioning
confidence: 90%
“…For example, basking sharks tracked in the English Channel displayed patterns of normal DVM in deep, well-stratified waters and reverse DVM in shallow, inner-shelf areas near thermal fronts, with such changes in behaviour between habitats likely mimicking those of zooplankton (Sims et al, 2005). Similarly, locational differences in DVM patterns have been recorded by reef manta rays, with normal DVM recorded around the islands and atolls of the Chagos archipelago (Andrzejaczek et al, 2020), and reverse DVM in both the coastal and offshore areas of the Red Sea (Braun et al, 2014), and the islands and atolls of Seychelles (Peel et al, 2020). Such diversity in foraging modes enables flexibility as these animals encounter spatial and/or temporal changes in the zooplanktonic prey community.…”
Section: Dvm In Filter-feeding Elasmobranchsmentioning
confidence: 90%
“…To better resolve horizontal movement patterns, future studies could combine passive acoustic telemetry with PSAT technology. Inclusion of acoustic tagging data can reduce the error associated with geolocation estimates (Peel et al ., 2020) and provide insight into more fine‐scale horizontal movement patterns. Future studies should focus on expanding tagging efforts further south in the Gulf of Mexico and the Caribbean.…”
Section: Discussionmentioning
confidence: 99%
“…For example, research on the reef manta ray Manta alfredi (Krefft 1868), a planktivorous coastal‐pelagic batoid, indicates that patterns of vertical movement vary by location. M. alfredi in the British Indian Ocean Territory exhibit diel vertical migration (DVM), occupying deeper mean diving depths during the day and moving up through the water column at night (Andrzejaczek et al ., 2019), whilst in the Red Sea and around the Seychelles M. alfredi remain closer to the surface during the day and dive deeper at night (Braun et al ., 2014; Peel et al ., 2020), a movement pattern known as reverse DVM. Both vertical movement strategies may be driven by foraging behaviour, with the contrasting patterns being attributed to regional oceanography affecting the distribution of their prey (Andrzejaczek et al ., 2020).…”
Section: Introductionmentioning
confidence: 99%
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“…For individuals tagged with MiniPATs, location estimates were processed using the manufacturer's software, Geolocation Processing Estimator 3 (GPE3; Wildlife Computers Inc., Redmond, WA, United States), which has been used widely to process elasmobranch tracking data (e.g., Skomal et al, 2017;Hutchinson et al, 2019;Peel et al, 2020). GPE3 generates two maximum likelihood position estimates per day using a hidden Markov model (Patterson et al, 2009) with a 0.25 • × 0.25 • grid spacing, and position estimates (two per day) interpolated and smoothed with a cubic spline.…”
Section: Satellite Trackingmentioning
confidence: 99%